Overview

Dataset statistics

Number of variables14
Number of observations26064
Missing cells16164
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with blade_angle and 7 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
blade_angle is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Nacelle ambient temperature (°C) is highly overall correlated with Metal particle count counterHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 7 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Metal particle count counter is highly overall correlated with Nacelle ambient temperature (°C)High correlation
blade_angle has 2250 (8.6%) missing valuesMissing
Rear bearing temperature (°C) has 2251 (8.6%) missing valuesMissing
Nacelle ambient temperature (°C) has 2251 (8.6%) missing valuesMissing
Front bearing temperature (°C) has 2251 (8.6%) missing valuesMissing
Tower Acceleration X (mm/ss) has 2251 (8.6%) missing valuesMissing
Tower Acceleration y (mm/ss) has 2251 (8.6%) missing valuesMissing
Metal particle count counter has 2251 (8.6%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 5343 (20.5%) zerosZeros
Rotor speed (RPM) has 3107 (11.9%) zerosZeros

Reproduction

Analysis started2023-07-08 12:01:56.865698
Analysis finished2023-07-08 12:02:13.407353
Duration16.54 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct26064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size203.8 KiB
Minimum2021-01-01 00:00:00
Maximum2021-06-30 23:50:00
2023-07-08T17:32:13.455216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:13.556804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct25776
Distinct (%)99.2%
Missing68
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean380.75832
Minimum-17.793049
Maximum2070.6698
Zeros1
Zeros (%)< 0.1%
Negative7807
Negative (%)30.0%
Memory size203.8 KiB
2023-07-08T17:32:13.666133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-17.793049
5-th percentile-4.4147551
Q1-1.35509
median142.24438
Q3484.60869
95-th percentile1877.4082
Maximum2070.6698
Range2088.4629
Interquartile range (IQR)485.96378

Descriptive statistics

Standard deviation554.17292
Coefficient of variation (CV)1.4554453
Kurtosis2.1618342
Mean380.75832
Median Absolute Deviation (MAD)144.67901
Skewness1.7760078
Sum9898193.3
Variance307107.62
MonotonicityNot monotonic
2023-07-08T17:32:13.763124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.679999948 6
 
< 0.1%
-1.649999976 6
 
< 0.1%
-0.7400000095 5
 
< 0.1%
-1.710000038 5
 
< 0.1%
-1.700000048 5
 
< 0.1%
-2.069999933 5
 
< 0.1%
-1.629999995 5
 
< 0.1%
-1.639999986 4
 
< 0.1%
-2.019999981 4
 
< 0.1%
-1.610000014 4
 
< 0.1%
Other values (25766) 25947
99.6%
(Missing) 68
 
0.3%
ValueCountFrequency (%)
-17.79304943 1
< 0.1%
-16.58091829 1
< 0.1%
-15.0804576 1
< 0.1%
-14.65778461 1
< 0.1%
-14.50358143 1
< 0.1%
-14.09489376 1
< 0.1%
-13.97000027 1
< 0.1%
-13.6249292 1
< 0.1%
-13.48563604 1
< 0.1%
-13.40016911 1
< 0.1%
ValueCountFrequency (%)
2070.669836 1
< 0.1%
2068.870758 1
< 0.1%
2067.282422 1
< 0.1%
2066.703401 1
< 0.1%
2066.2276 1
< 0.1%
2065.827417 1
< 0.1%
2065.275281 1
< 0.1%
2065.164307 1
< 0.1%
2065.058105 1
< 0.1%
2065.026099 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct25813
Distinct (%)99.3%
Missing68
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean179.54248
Minimum0.016783842
Maximum359.99569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:13.976003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.016783842
5-th percentile17.290669
Q171.485135
median200.39622
Q3263.4128
95-th percentile333.88392
Maximum359.99569
Range359.97891
Interquartile range (IQR)191.92767

Descriptive statistics

Standard deviation104.9364
Coefficient of variation (CV)0.58446558
Kurtosis-1.2753382
Mean179.54248
Median Absolute Deviation (MAD)87.844218
Skewness-0.17054155
Sum4667386.2
Variance11011.647
MonotonicityNot monotonic
2023-07-08T17:32:14.074003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.31999969 3
 
< 0.1%
42.95000076 3
 
< 0.1%
30.70999908 3
 
< 0.1%
30.55999947 3
 
< 0.1%
51.81000137 3
 
< 0.1%
33.52000046 3
 
< 0.1%
359.769989 3
 
< 0.1%
30.62999916 3
 
< 0.1%
30.54000092 3
 
< 0.1%
53.97999954 3
 
< 0.1%
Other values (25803) 25966
99.6%
(Missing) 68
 
0.3%
ValueCountFrequency (%)
0.01678384197 1
< 0.1%
0.06145831644 1
< 0.1%
0.06541140759 1
< 0.1%
0.06962438147 1
< 0.1%
0.07448541406 1
< 0.1%
0.1048552225 1
< 0.1%
0.1084696931 1
< 0.1%
0.1376942648 1
< 0.1%
0.143737817 1
< 0.1%
0.1964100543 1
< 0.1%
ValueCountFrequency (%)
359.9956918 1
< 0.1%
359.9713922 1
< 0.1%
359.9671448 1
< 0.1%
359.9620181 1
< 0.1%
359.9619294 1
< 0.1%
359.951662 1
< 0.1%
359.9258437 1
< 0.1%
359.9162732 1
< 0.1%
359.9066901 1
< 0.1%
359.9038691 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct6376
Distinct (%)24.5%
Missing68
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean180.95629
Minimum0.097747057
Maximum359.89123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:14.183039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.097747057
5-th percentile18.870001
Q178.891306
median201.06906
Q3265.82492
95-th percentile333.87433
Maximum359.89123
Range359.79348
Interquartile range (IQR)186.93362

Descriptive statistics

Standard deviation104.81394
Coefficient of variation (CV)0.5792224
Kurtosis-1.2746691
Mean180.95629
Median Absolute Deviation (MAD)89.999847
Skewness-0.17224737
Sum4704139.7
Variance10985.961
MonotonicityNot monotonic
2023-07-08T17:32:14.283983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79.24028778 586
 
2.2%
47.41055679 344
 
1.3%
194.4837036 289
 
1.1%
94.60556793 190
 
0.7%
193.3861389 185
 
0.7%
292.1665039 180
 
0.7%
167.0446014 127
 
0.5%
278.9961853 123
 
0.5%
206.5569305 109
 
0.4%
29.84957886 108
 
0.4%
Other values (6366) 23755
91.1%
ValueCountFrequency (%)
0.09774705738 1
 
< 0.1%
0.1086353916 1
 
< 0.1%
0.1199999973 1
 
< 0.1%
0.1400000006 1
 
< 0.1%
0.1655835111 1
 
< 0.1%
0.1961488951 1
 
< 0.1%
0.2155139446 45
0.2%
0.2158801556 2
 
< 0.1%
0.2159729004 7
 
< 0.1%
0.2164916992 3
 
< 0.1%
ValueCountFrequency (%)
359.8912304 1
< 0.1%
359.7182504 1
< 0.1%
359.7041182 1
< 0.1%
359.6499939 1
< 0.1%
359.6023252 1
< 0.1%
359.6017609 1
< 0.1%
359.590003 1
< 0.1%
359.4567296 1
< 0.1%
359.351395 1
< 0.1%
359.2683807 1
< 0.1%

blade_angle
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8333
Distinct (%)35.0%
Missing2250
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean23.191487
Minimum0
Maximum92.493332
Zeros5343
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:14.391460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.025964913
median1.0728333
Q344.993334
95-th percentile91.869998
Maximum92.493332
Range92.493332
Interquartile range (IQR)44.96737

Descriptive statistics

Standard deviation35.245452
Coefficient of variation (CV)1.5197582
Kurtosis-0.29337532
Mean23.191487
Median Absolute Deviation (MAD)1.0728333
Skewness1.1867665
Sum552282.06
Variance1242.2419
MonotonicityNot monotonic
2023-07-08T17:32:14.492483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5343
20.5%
91.86999766 3598
 
13.8%
44.99333445 2164
 
8.3%
1.49333334 664
 
2.5%
89.99333191 530
 
2.0%
0.02466666698 307
 
1.2%
61.98333271 173
 
0.7%
0.04933333397 124
 
0.5%
0.07400000095 90
 
0.3%
0.4933333397 80
 
0.3%
Other values (8323) 10741
41.2%
(Missing) 2250
 
8.6%
ValueCountFrequency (%)
0 5343
20.5%
0.0001666666629 3
 
< 0.1%
0.0003333333259 8
 
< 0.1%
0.0004999999888 7
 
< 0.1%
0.0005263157777 1
 
< 0.1%
0.0006666666518 2
 
< 0.1%
0.0008333333147 1
 
< 0.1%
0.0008596491036 1
 
< 0.1%
0.0009259259052 1
 
< 0.1%
0.0009999999776 3
 
< 0.1%
ValueCountFrequency (%)
92.49333191 54
 
0.2%
92.40999858 1
 
< 0.1%
92.40999858 1
 
< 0.1%
92.24333191 1
 
< 0.1%
92.13949992 1
 
< 0.1%
91.99719426 1
 
< 0.1%
91.8700002 7
 
< 0.1%
91.86999766 3598
13.8%
91.86333211 4
 
< 0.1%
91.86333211 16
 
0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19643
Distinct (%)82.5%
Missing2251
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean52.509345
Minimum5.4000001
Maximum73.8675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:14.594130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.4000001
5-th percentile10.178476
Q142.019999
median63.17
Q367.8125
95-th percentile70.1725
Maximum73.8675
Range68.4675
Interquartile range (IQR)25.792502

Descriptive statistics

Standard deviation21.017789
Coefficient of variation (CV)0.4002676
Kurtosis-0.31930763
Mean52.509345
Median Absolute Deviation (MAD)6.0524996
Skewness-1.1174309
Sum1250405
Variance441.74747
MonotonicityNot monotonic
2023-07-08T17:32:14.692047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.60000038 51
 
0.2%
7.5 50
 
0.2%
6.099999905 40
 
0.2%
7.099999905 35
 
0.1%
6.699999809 35
 
0.1%
7.400000095 33
 
0.1%
12.80000019 33
 
0.1%
13.30000019 32
 
0.1%
12.5 29
 
0.1%
7 29
 
0.1%
Other values (19633) 23446
90.0%
(Missing) 2251
 
8.6%
ValueCountFrequency (%)
5.400000095 10
< 0.1%
5.405000091 1
 
< 0.1%
5.410000086 1
 
< 0.1%
5.440000057 1
 
< 0.1%
5.549999952 1
 
< 0.1%
5.574999928 1
 
< 0.1%
5.599999905 16
0.1%
5.604999876 1
 
< 0.1%
5.649999785 1
 
< 0.1%
5.677499795 1
 
< 0.1%
ValueCountFrequency (%)
73.86749992 1
< 0.1%
73.16500015 1
< 0.1%
73.13250008 1
< 0.1%
73.05526332 1
< 0.1%
73.05249939 1
< 0.1%
72.92500038 1
< 0.1%
72.90249939 1
< 0.1%
72.72000008 1
< 0.1%
72.67999878 1
< 0.1%
72.58249893 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21026
Distinct (%)80.9%
Missing68
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean7.8707491
Minimum0
Maximum15.276603
Zeros3107
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:14.797665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.77090016
median9.3400002
Q311.383684
95-th percentile15.133228
Maximum15.276603
Range15.276603
Interquartile range (IQR)10.612784

Descriptive statistics

Standard deviation5.2430351
Coefficient of variation (CV)0.66614182
Kurtosis-1.1786455
Mean7.8707491
Median Absolute Deviation (MAD)3.15519
Skewness-0.43975451
Sum204607.99
Variance27.489417
MonotonicityNot monotonic
2023-07-08T17:32:14.900821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3107
 
11.9%
8.989999771 101
 
0.4%
9 47
 
0.2%
9.010000229 38
 
0.1%
0.0110000018 38
 
0.1%
0.01050000242 35
 
0.1%
0.01150000188 32
 
0.1%
0.01250000205 31
 
0.1%
0.01200000197 27
 
0.1%
9.029999733 25
 
0.1%
Other values (21016) 22515
86.4%
(Missing) 68
 
0.3%
ValueCountFrequency (%)
0 3107
11.9%
0.000264500035 1
 
< 0.1%
0.0002750000494 1
 
< 0.1%
0.0003080000824 1
 
< 0.1%
0.0008820001676 1
 
< 0.1%
0.001362500276 1
 
< 0.1%
0.001428000134 1
 
< 0.1%
0.001596000278 1
 
< 0.1%
0.001738000195 1
 
< 0.1%
0.001912500418 1
 
< 0.1%
ValueCountFrequency (%)
15.27660298 1
< 0.1%
15.27469696 1
< 0.1%
15.26987842 1
< 0.1%
15.26948637 1
< 0.1%
15.26913701 1
< 0.1%
15.26837929 1
< 0.1%
15.26620459 1
< 0.1%
15.26364103 1
< 0.1%
15.26108368 1
< 0.1%
15.26037116 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct25847
Distinct (%)99.4%
Missing68
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean934.46669
Minimum-0.20651846
Maximum1814.5141
Zeros28
Zeros (%)0.1%
Negative69
Negative (%)0.3%
Memory size203.8 KiB
2023-07-08T17:32:15.010893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.20651846
5-th percentile0.48998686
Q190.905564
median1109.0455
Q31351.4859
95-th percentile1793.2581
Maximum1814.5141
Range1814.7206
Interquartile range (IQR)1260.5803

Descriptive statistics

Standard deviation621.31807
Coefficient of variation (CV)0.66489055
Kurtosis-1.1802537
Mean934.46669
Median Absolute Deviation (MAD)373.66655
Skewness-0.44183047
Sum24292396
Variance386036.15
MonotonicityNot monotonic
2023-07-08T17:32:15.110585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 28
 
0.1%
1070.910034 7
 
< 0.1%
1070.939941 6
 
< 0.1%
1071.050049 6
 
< 0.1%
1070.849976 5
 
< 0.1%
1070.859985 4
 
< 0.1%
1070.869995 4
 
< 0.1%
1070.959961 4
 
< 0.1%
1070.98999 4
 
< 0.1%
1071.02002 3
 
< 0.1%
Other values (25837) 25925
99.5%
(Missing) 68
 
0.3%
ValueCountFrequency (%)
-0.2065184563 1
< 0.1%
-0.1962261668 1
< 0.1%
-0.1720348891 1
< 0.1%
-0.1712871613 1
< 0.1%
-0.1569923137 1
< 0.1%
-0.1562005995 1
< 0.1%
-0.1535835459 1
< 0.1%
-0.1514283204 1
< 0.1%
-0.1496909471 1
< 0.1%
-0.1370015235 1
< 0.1%
ValueCountFrequency (%)
1814.514078 1
< 0.1%
1811.338274 1
< 0.1%
1810.983685 1
< 0.1%
1810.623447 1
< 0.1%
1810.280085 1
< 0.1%
1809.687939 1
< 0.1%
1807.786228 1
< 0.1%
1807.233971 1
< 0.1%
1807.124434 1
< 0.1%
1807.014084 1
< 0.1%

Nacelle ambient temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17502
Distinct (%)73.5%
Missing2251
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean8.7001102
Minimum-2.7025
Maximum27.35
Zeros0
Zeros (%)0.0%
Negative753
Negative (%)2.9%
Memory size203.8 KiB
2023-07-08T17:32:15.215812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.7025
5-th percentile0.80500001
Q14.8350002
median8.28
Q311.565
95-th percentile19.5345
Maximum27.35
Range30.0525
Interquartile range (IQR)6.7299998

Descriptive statistics

Standard deviation5.4567951
Coefficient of variation (CV)0.62720988
Kurtosis0.13274137
Mean8.7001102
Median Absolute Deviation (MAD)3.3799999
Skewness0.57989256
Sum207175.72
Variance29.776612
MonotonicityNot monotonic
2023-07-08T17:32:15.312884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.099999905 54
 
0.2%
1.399999976 47
 
0.2%
8.5 44
 
0.2%
10.60000038 40
 
0.2%
4.199999809 40
 
0.2%
4.599999905 39
 
0.1%
8.800000191 39
 
0.1%
9.600000381 38
 
0.1%
7 37
 
0.1%
2.299999952 37
 
0.1%
Other values (17492) 23398
89.8%
(Missing) 2251
 
8.6%
ValueCountFrequency (%)
-2.702500021 1
 
< 0.1%
-2.700000048 3
< 0.1%
-2.700000024 1
 
< 0.1%
-2.6625 1
 
< 0.1%
-2.657499993 1
 
< 0.1%
-2.632499957 1
 
< 0.1%
-2.617499936 1
 
< 0.1%
-2.613157824 1
 
< 0.1%
-2.604999912 1
 
< 0.1%
-2.604999912 1
 
< 0.1%
ValueCountFrequency (%)
27.3499999 1
< 0.1%
27.15500002 1
< 0.1%
27.11000023 1
< 0.1%
26.98249989 1
< 0.1%
26.82105265 1
< 0.1%
26.72105257 1
< 0.1%
26.66500044 1
< 0.1%
26.59250031 1
< 0.1%
26.52250013 1
< 0.1%
26.52000017 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19239
Distinct (%)80.8%
Missing2251
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean53.712911
Minimum5.6999998
Maximum84.867501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:15.418028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.6999998
5-th percentile9.1979998
Q142.2025
median62.453572
Q371.5225
95-th percentile74.6535
Maximum84.867501
Range79.167501
Interquartile range (IQR)29.32

Descriptive statistics

Standard deviation22.376806
Coefficient of variation (CV)0.41660014
Kurtosis-0.47147408
Mean53.712911
Median Absolute Deviation (MAD)10.496427
Skewness-0.97298482
Sum1279065.5
Variance500.72146
MonotonicityNot monotonic
2023-07-08T17:32:15.518930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.80000019 73
 
0.3%
9.300000191 66
 
0.3%
12 59
 
0.2%
14.60000038 57
 
0.2%
9.699999809 56
 
0.2%
14.89999962 54
 
0.2%
8.800000191 53
 
0.2%
9.199999809 52
 
0.2%
13.19999981 50
 
0.2%
12.39999962 49
 
0.2%
Other values (19229) 23244
89.2%
(Missing) 2251
 
8.6%
ValueCountFrequency (%)
5.699999809 25
0.1%
5.704999828 1
 
< 0.1%
5.724999881 1
 
< 0.1%
5.747058784 1
 
< 0.1%
5.88500011 1
 
< 0.1%
5.88947379 1
 
< 0.1%
5.900000095 13
< 0.1%
5.902500081 1
 
< 0.1%
5.954999971 1
 
< 0.1%
5.982499981 1
 
< 0.1%
ValueCountFrequency (%)
84.86750069 1
< 0.1%
84.63500099 1
< 0.1%
84.54250031 1
< 0.1%
84.51750069 1
< 0.1%
84.40500031 1
< 0.1%
84.32499962 1
< 0.1%
84.24250107 1
< 0.1%
84.18000031 1
< 0.1%
84.04750023 1
< 0.1%
83.95999947 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23811
Distinct (%)> 99.9%
Missing2251
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean57.19936
Minimum3.2304296
Maximum258.74933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:15.626678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.2304296
5-th percentile4.3951326
Q115.442324
median57.399528
Q387.107755
95-th percentile127.87483
Maximum258.74933
Range255.5189
Interquartile range (IQR)71.665432

Descriptive statistics

Standard deviation41.461716
Coefficient of variation (CV)0.72486329
Kurtosis-0.55577056
Mean57.19936
Median Absolute Deviation (MAD)35.645174
Skewness0.40376458
Sum1362088.4
Variance1719.0739
MonotonicityNot monotonic
2023-07-08T17:32:15.730019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.780908859 2
 
< 0.1%
4.389092642 2
 
< 0.1%
68.87216425 1
 
< 0.1%
89.06661334 1
 
< 0.1%
65.5804884 1
 
< 0.1%
92.58496161 1
 
< 0.1%
81.01624889 1
 
< 0.1%
81.71848807 1
 
< 0.1%
83.18411989 1
 
< 0.1%
89.37967319 1
 
< 0.1%
Other values (23801) 23801
91.3%
(Missing) 2251
 
8.6%
ValueCountFrequency (%)
3.230429587 1
< 0.1%
3.278984118 1
< 0.1%
3.299136353 1
< 0.1%
3.301521975 1
< 0.1%
3.311457747 1
< 0.1%
3.351369041 1
< 0.1%
3.351397192 1
< 0.1%
3.353838986 1
< 0.1%
3.360350525 1
< 0.1%
3.378509951 1
< 0.1%
ValueCountFrequency (%)
258.749333 1
< 0.1%
254.9615417 1
< 0.1%
253.4124105 1
< 0.1%
227.171246 1
< 0.1%
224.4503983 1
< 0.1%
221.1097996 1
< 0.1%
216.0026928 1
< 0.1%
213.6454262 1
< 0.1%
212.9129272 1
< 0.1%
209.9513748 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct24500
Distinct (%)94.2%
Missing68
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.5106852
Minimum0.14190037
Maximum20.685407
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:15.834776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.14190037
5-th percentile1.9106142
Q13.53
median5.0308055
Q36.9283074
95-th percentile10.999072
Maximum20.685407
Range20.543507
Interquartile range (IQR)3.3983075

Descriptive statistics

Standard deviation2.7341516
Coefficient of variation (CV)0.49615456
Kurtosis0.58737626
Mean5.5106852
Median Absolute Deviation (MAD)1.6610189
Skewness0.85666264
Sum143255.77
Variance7.4755849
MonotonicityNot monotonic
2023-07-08T17:32:15.934077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.859999895 12
 
< 0.1%
4.480000019 10
 
< 0.1%
3.799999952 10
 
< 0.1%
5.849999905 10
 
< 0.1%
3.920000076 9
 
< 0.1%
4.920000076 9
 
< 0.1%
4.769999981 9
 
< 0.1%
5.449999809 9
 
< 0.1%
4.829999924 9
 
< 0.1%
3.900000095 9
 
< 0.1%
Other values (24490) 25900
99.4%
(Missing) 68
 
0.3%
ValueCountFrequency (%)
0.141900368 1
< 0.1%
0.1440191215 1
< 0.1%
0.1656563211 1
< 0.1%
0.2307564983 1
< 0.1%
0.2407689951 1
< 0.1%
0.2668688834 1
< 0.1%
0.2767878044 1
< 0.1%
0.2785313591 1
< 0.1%
0.2934001371 1
< 0.1%
0.3155438494 1
< 0.1%
ValueCountFrequency (%)
20.68540745 1
< 0.1%
19.49710999 1
< 0.1%
18.96768918 1
< 0.1%
18.8512466 1
< 0.1%
18.54310913 1
< 0.1%
18.35744729 1
< 0.1%
18.35149829 1
< 0.1%
18.12782915 1
< 0.1%
17.86032472 1
< 0.1%
17.82483315 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23813
Distinct (%)100.0%
Missing2251
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean26.32983
Minimum3.129078
Maximum180.55096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:16.154623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.129078
5-th percentile4.3941729
Q114.005823
median24.077637
Q334.775761
95-th percentile59.014259
Maximum180.55096
Range177.42188
Interquartile range (IQR)20.769938

Descriptive statistics

Standard deviation17.583863
Coefficient of variation (CV)0.66783049
Kurtosis3.7022312
Mean26.32983
Median Absolute Deviation (MAD)10.405271
Skewness1.3461909
Sum626992.25
Variance309.19225
MonotonicityNot monotonic
2023-07-08T17:32:16.255254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.76577613 1
 
< 0.1%
29.89678118 1
 
< 0.1%
29.06229088 1
 
< 0.1%
26.4293484 1
 
< 0.1%
26.55651994 1
 
< 0.1%
38.60742159 1
 
< 0.1%
34.94579158 1
 
< 0.1%
29.58099909 1
 
< 0.1%
34.11321058 1
 
< 0.1%
27.29066851 1
 
< 0.1%
Other values (23803) 23803
91.3%
(Missing) 2251
 
8.6%
ValueCountFrequency (%)
3.129077953 1
< 0.1%
3.209826946 1
< 0.1%
3.212493283 1
< 0.1%
3.352716386 1
< 0.1%
3.371189816 1
< 0.1%
3.37991333 1
< 0.1%
3.380734384 1
< 0.1%
3.400065547 1
< 0.1%
3.430125242 1
< 0.1%
3.434363496 1
< 0.1%
ValueCountFrequency (%)
180.5509563 1
< 0.1%
168.7860471 1
< 0.1%
161.1588326 1
< 0.1%
159.9681723 1
< 0.1%
154.1729106 1
< 0.1%
152.2964605 1
< 0.1%
149.9124987 1
< 0.1%
148.2666744 1
< 0.1%
147.1515694 1
< 0.1%
144.199365 1
< 0.1%

Metal particle count counter
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)0.1%
Missing2251
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean648.26507
Minimum636
Maximum658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size203.8 KiB
2023-07-08T17:32:16.352768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum636
5-th percentile636
Q1643
median649
Q3653
95-th percentile658
Maximum658
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.6165935
Coefficient of variation (CV)0.010206617
Kurtosis-1.0216129
Mean648.26507
Median Absolute Deviation (MAD)6
Skewness-0.33003167
Sum15437136
Variance43.779309
MonotonicityIncreasing
2023-07-08T17:32:16.432333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
643 5687
21.8%
652 5152
19.8%
648 2568
9.9%
636 1944
 
7.5%
655 1915
 
7.3%
657 1710
 
6.6%
658 1263
 
4.8%
637 583
 
2.2%
640 577
 
2.2%
651 501
 
1.9%
Other values (10) 1913
 
7.3%
(Missing) 2251
 
8.6%
ValueCountFrequency (%)
636 1944
 
7.5%
637 583
 
2.2%
638 87
 
0.3%
640 577
 
2.2%
641 155
 
0.6%
642 70
 
0.3%
643 5687
21.8%
645 3
 
< 0.1%
647 8
 
< 0.1%
648 2568
9.9%
ValueCountFrequency (%)
658 1263
 
4.8%
657 1710
 
6.6%
656 416
 
1.6%
655 1915
 
7.3%
654 317
 
1.2%
653 468
 
1.8%
652 5152
19.8%
651 501
 
1.9%
650 1
 
< 0.1%
649 388
 
1.5%

Interactions

2023-07-08T17:32:11.713751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:57.618841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.722741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.015192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.176173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.260823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.512071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.684161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.840966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.072211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.230010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.351362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.439758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.791712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:57.702745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.807208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.100709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.254105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.342283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.595707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.766904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.921813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.154309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.311087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.427849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.524731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.880901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:57.786700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.001577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.193113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.340331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.434878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.689925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.859189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.010444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.247462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.399111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.516490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.615918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.968476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:57.874311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.093413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.286005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.428539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.525704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.782691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.952895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.101175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.340663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.489385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.603051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.709578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.048028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:57.949491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.178570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.369985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.503885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.607670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.867535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.035280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.180491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.423052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.568813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.681848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.791855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.133937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.032631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.272481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.462678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.587696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.694972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.958411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.127227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.267319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.513985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.658450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.766612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.883858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.224528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.120960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.374066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.558926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.678254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.788435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.054501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.222053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.360615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.608891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.750044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.858003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.978414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.313853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.205032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.469792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.651085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.764172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.988907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.146768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.313326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.449459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.702159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.841999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.943982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.189319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.396457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.292393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.563088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.737960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.846495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.073669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.236600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.400098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.533541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.788511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.925985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.024749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.272911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.487375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.382801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.658130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.832200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.933617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.165931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.330137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.494622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.623900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.881433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.016198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.114211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.366960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.569648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.469857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.748388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.917407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.015816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.252689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.419681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.580713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.709210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:07.968102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.099798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.195139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.452846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.650047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.549983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.832371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:00.999536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.093177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.334369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.503558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.662661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.788522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.051066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.179438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.272920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.537017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:12.738338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:58.639200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:31:59.927799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:01.092792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:02.179833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:03.425874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:04.598715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:05.756650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:06.878896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:08.145201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:09.269553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:10.359838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:32:11.627652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:32:16.511678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.1340.116-0.7410.8840.9560.9530.0900.9500.7640.6170.6930.087
Wind direction (°)0.1341.0000.854-0.1610.2050.1400.1380.1030.2090.2510.0210.1750.059
Nacelle position (°)0.1160.8541.000-0.1370.1830.1180.1150.0910.1870.2240.0130.1480.050
blade_angle-0.741-0.161-0.1371.000-0.785-0.746-0.745-0.002-0.767-0.605-0.251-0.409-0.162
Rear bearing temperature (°C)0.8840.2050.183-0.7851.0000.8950.8910.1750.9430.7380.4390.5980.224
Rotor speed (RPM)0.9560.1400.118-0.7460.8951.0000.9990.0610.9590.8110.5950.7370.110
Generator RPM (RPM)0.9530.1380.115-0.7450.8910.9991.0000.0490.9560.8120.6010.7390.101
Nacelle ambient temperature (°C)0.0900.1030.091-0.0020.1750.0610.0491.0000.1080.048-0.0880.0210.671
Front bearing temperature (°C)0.9500.2090.187-0.7670.9430.9590.9560.1081.0000.7580.5220.6650.148
Tower Acceleration X (mm/ss)0.7640.2510.224-0.6050.7380.8110.8120.0480.7581.0000.4510.8250.102
Wind speed (m/s)0.6170.0210.013-0.2510.4390.5950.601-0.0880.5220.4511.0000.731-0.233
Tower Acceleration y (mm/ss)0.6930.1750.148-0.4090.5980.7370.7390.0210.6650.8250.7311.000-0.037
Metal particle count counter0.0870.0590.050-0.1620.2240.1100.1010.6710.1480.102-0.233-0.0371.000

Missing values

2023-07-08T17:32:12.857795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:32:13.052114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:32:13.258722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02021-01-01 00:00:00176.543091308.514244286.678680.42100061.5049999.5020761127.9314791.88750060.030000120.1425514.03801938.765776636.0
12021-01-01 00:10:00119.758620305.997476286.678680.79333361.3125009.5597781136.9581831.50500059.787500112.8933333.71074329.165829636.0
22021-01-01 00:20:00142.842966302.037541286.678680.71383361.2625009.5223311131.8752380.88750059.65750095.5492064.11807929.821561636.0
32021-01-01 00:30:00181.747184300.810472286.678680.27133362.0400009.4310671121.0402470.29750061.15750095.5292524.36587033.539071636.0
42021-01-01 00:40:00178.464342291.378454286.678680.37600062.0400009.4285091120.0740680.26750061.21499972.0168254.37674725.737522636.0
52021-01-01 00:50:00255.708743285.200884286.678680.07433362.8675009.5923921139.6854360.34000062.80000056.4213605.18813323.655984636.0
62021-01-01 01:00:00206.381018282.293825286.678680.11869362.8205889.2501011099.1684090.49411863.15882358.9240425.02820726.458409636.0
72021-01-01 01:10:00172.280673290.560164286.678680.42715862.1575009.4388971119.3901520.66500061.74249979.6885084.71090930.215060636.0
82021-01-01 01:20:00156.851626291.508669286.678680.41966762.2225009.3459301110.6172430.51500061.57749987.2044214.43947023.384408636.0
92021-01-01 01:30:0083.398749297.806340286.678681.19316761.2450009.5116521129.2468050.57750060.04750092.5223993.73339025.066440636.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
260542021-06-30 22:20:000.56000028.78000134.240002NaNNaN8.991071.020020NaNNaNNaN2.91NaNNaN
260552021-06-30 22:30:004.97000029.90000034.240002NaNNaN8.991070.849976NaNNaNNaN3.02NaNNaN
260562021-06-30 22:40:00-7.50000030.37000134.240002NaNNaN5.22620.159973NaNNaNNaN2.52NaNNaN
260572021-06-30 22:50:00-1.68000024.62000134.240002NaNNaN0.7488.589996NaNNaNNaN2.24NaNNaN
260582021-06-30 23:00:00-1.74000024.86000134.240002NaNNaN0.8095.379997NaNNaNNaN2.39NaNNaN
260592021-06-30 23:10:009.88000033.33000234.240002NaNNaN4.26508.429993NaNNaNNaN3.17NaNNaN
260602021-06-30 23:20:0052.25000033.54000134.240002NaNNaN8.991070.920044NaNNaNNaN3.64NaNNaN
260612021-06-30 23:30:0040.95000136.90000234.240002NaNNaN8.991070.880005NaNNaNNaN3.53NaNNaN
260622021-06-30 23:40:0017.23000029.35000034.240002NaNNaN8.991070.829956NaNNaNNaN3.10NaNNaN
260632021-06-30 23:50:00-13.97000029.74000034.240002NaNNaN8.991070.890015NaNNaNNaN2.36NaNNaN